Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "38" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 29 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 29 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460012 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.736913 | 0.552961 | -1.392782 | 2.845097 | -0.126804 | -0.338033 | 2.970894 | 15.750417 | 0.5727 | 0.5584 | 0.3693 | nan | nan |
| 2460011 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.894880 | 0.285499 | -1.973098 | 3.626209 | -0.514629 | -0.656463 | 3.695974 | 15.906928 | 0.5799 | 0.5632 | 0.3715 | nan | nan |
| 2460010 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.911332 | 0.519506 | -1.209381 | 3.220055 | 0.147808 | 0.584123 | 1.719204 | 10.593939 | 0.5842 | 0.5696 | 0.3760 | nan | nan |
| 2460009 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.800415 | -0.006744 | -1.576652 | 3.623628 | -0.389501 | -0.091646 | 3.611674 | 9.870052 | 0.5920 | 0.5814 | 0.3763 | nan | nan |
| 2460008 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.910820 | 0.228307 | -1.704522 | 3.935251 | -0.437776 | 0.306930 | -0.413772 | 8.019540 | 0.6304 | 0.6282 | 0.3423 | nan | nan |
| 2460007 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.780984 | 0.038991 | -1.451025 | 3.055092 | -0.054424 | 0.430548 | 1.333463 | 10.011777 | 0.5980 | 0.5879 | 0.3620 | nan | nan |
| 2459999 | digital_ok | 0.00% | 99.58% | 99.42% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.3139 | 0.3782 | 0.2460 | nan | nan |
| 2459998 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.383474 | 0.162866 | -1.219273 | 2.559185 | -0.031145 | 0.482156 | 3.678357 | 14.411470 | 0.6019 | 0.5936 | 0.3947 | nan | nan |
| 2459997 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.592481 | 0.299605 | -1.186687 | 2.962155 | -0.087781 | 0.067646 | 6.302130 | 15.961542 | 0.6145 | 0.6103 | 0.3912 | nan | nan |
| 2459996 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.451055 | 0.306416 | -1.207422 | 3.529648 | -0.097549 | 0.145470 | 5.082399 | 8.615295 | 0.6224 | 0.6139 | 0.4066 | nan | nan |
| 2459995 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.184864 | -0.053763 | 0.539511 | 0.621247 | -0.256130 | 0.496980 | 5.193437 | 1.672618 | 0.6068 | 0.6111 | 0.3965 | nan | nan |
| 2459994 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.239936 | -0.133145 | 0.417373 | 0.811027 | -0.168973 | 0.315070 | 2.951971 | 0.943238 | 0.6039 | 0.6061 | 0.3908 | nan | nan |
| 2459993 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.188198 | 0.031792 | 0.170296 | 0.561623 | -0.488750 | 0.331068 | 2.783455 | 0.898314 | 0.5871 | 0.6104 | 0.4012 | nan | nan |
| 2459991 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.125314 | 0.002474 | 0.288544 | 0.672534 | -0.011074 | 0.330968 | 3.634122 | 1.028847 | 0.6201 | 0.6132 | 0.3989 | nan | nan |
| 2459990 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.386767 | 0.169336 | 0.265484 | 0.600487 | -0.116886 | 0.120208 | 6.316434 | 2.271169 | 0.6146 | 0.6121 | 0.3942 | nan | nan |
| 2459989 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.069539 | 0.073185 | 0.233085 | 0.805875 | -0.123833 | 0.524103 | 4.809305 | 1.773806 | 0.6112 | 0.6111 | 0.3949 | nan | nan |
| 2459988 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.356704 | 0.019989 | 0.237188 | 0.533179 | 0.048150 | 0.003702 | 3.417760 | 0.901693 | 0.6116 | 0.6100 | 0.3884 | nan | nan |
| 2459987 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.317197 | -0.078388 | 0.310857 | 0.736502 | 0.287114 | 0.802403 | 5.910790 | 2.475566 | 0.6211 | 0.6175 | 0.3872 | nan | nan |
| 2459986 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.277715 | -0.032529 | 0.344222 | 0.651463 | -0.054660 | 0.509781 | 3.081818 | 0.996880 | 0.6432 | 0.6437 | 0.3383 | nan | nan |
| 2459985 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.683878 | 0.117275 | 0.372227 | 0.707551 | -0.244354 | 0.148243 | 5.650194 | 2.739923 | 0.6193 | 0.6151 | 0.3944 | nan | nan |
| 2459984 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.327538 | 0.416729 | 0.498445 | 0.709575 | 1.286902 | 1.054821 | 4.254054 | 1.129675 | 0.6325 | 0.6302 | 0.3735 | nan | nan |
| 2459983 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.023984 | 0.112546 | 0.326101 | 0.519696 | 0.358524 | 0.895500 | 3.599117 | 0.958826 | 0.6379 | 0.6570 | 0.3427 | nan | nan |
| 2459982 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.189377 | 0.315704 | 0.423529 | 0.678306 | -0.003534 | 0.823007 | 0.761790 | 0.720404 | 0.7120 | 0.7068 | 0.2867 | nan | nan |
| 2459981 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.036376 | 0.085710 | 0.208954 | 0.439197 | 0.124947 | 0.671797 | 3.090624 | 0.709424 | 0.6213 | 0.6299 | 0.3951 | nan | nan |
| 2459980 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.048573 | -0.080045 | 0.122211 | 0.542039 | 0.011845 | 0.667311 | 1.384075 | 1.126327 | 0.6710 | 0.6765 | 0.3100 | nan | nan |
| 2459979 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.420854 | -0.232172 | -0.046775 | 0.474453 | -0.281477 | 0.738807 | 4.870564 | 1.052139 | 0.6090 | 0.6217 | 0.3907 | nan | nan |
| 2459978 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.421470 | -0.535955 | 0.010561 | 0.495958 | -0.072713 | 0.739030 | 6.871010 | 1.362586 | 0.6115 | 0.6244 | 0.4003 | nan | nan |
| 2459977 | digital_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.088798 | -0.357518 | 0.105867 | 0.503497 | 0.066897 | 1.406270 | 3.430858 | 0.565892 | 0.5723 | 0.5847 | 0.3635 | nan | nan |
| 2459976 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.083115 | -0.280914 | 0.126652 | 0.535461 | 0.071087 | 0.491034 | 4.499473 | 1.110325 | 0.6196 | 0.6313 | 0.3864 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Discontinuties | 15.750417 | -0.736913 | 0.552961 | -1.392782 | 2.845097 | -0.126804 | -0.338033 | 2.970894 | 15.750417 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Discontinuties | 15.906928 | -0.894880 | 0.285499 | -1.973098 | 3.626209 | -0.514629 | -0.656463 | 3.695974 | 15.906928 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Discontinuties | 10.593939 | -0.911332 | 0.519506 | -1.209381 | 3.220055 | 0.147808 | 0.584123 | 1.719204 | 10.593939 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Discontinuties | 9.870052 | -0.800415 | -0.006744 | -1.576652 | 3.623628 | -0.389501 | -0.091646 | 3.611674 | 9.870052 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Discontinuties | 8.019540 | 0.228307 | -0.910820 | 3.935251 | -1.704522 | 0.306930 | -0.437776 | 8.019540 | -0.413772 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Discontinuties | 10.011777 | -0.780984 | 0.038991 | -1.451025 | 3.055092 | -0.054424 | 0.430548 | 1.333463 | 10.011777 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Discontinuties | 14.411470 | -0.383474 | 0.162866 | -1.219273 | 2.559185 | -0.031145 | 0.482156 | 3.678357 | 14.411470 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Discontinuties | 15.961542 | -0.592481 | 0.299605 | -1.186687 | 2.962155 | -0.087781 | 0.067646 | 6.302130 | 15.961542 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Discontinuties | 8.615295 | -0.451055 | 0.306416 | -1.207422 | 3.529648 | -0.097549 | 0.145470 | 5.082399 | 8.615295 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 5.193437 | 0.184864 | -0.053763 | 0.539511 | 0.621247 | -0.256130 | 0.496980 | 5.193437 | 1.672618 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 2.951971 | 0.239936 | -0.133145 | 0.417373 | 0.811027 | -0.168973 | 0.315070 | 2.951971 | 0.943238 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 2.783455 | 0.188198 | 0.031792 | 0.170296 | 0.561623 | -0.488750 | 0.331068 | 2.783455 | 0.898314 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 3.634122 | -0.125314 | 0.002474 | 0.288544 | 0.672534 | -0.011074 | 0.330968 | 3.634122 | 1.028847 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 6.316434 | 0.169336 | -0.386767 | 0.600487 | 0.265484 | 0.120208 | -0.116886 | 2.271169 | 6.316434 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 4.809305 | 0.073185 | -0.069539 | 0.805875 | 0.233085 | 0.524103 | -0.123833 | 1.773806 | 4.809305 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 3.417760 | 0.019989 | 0.356704 | 0.533179 | 0.237188 | 0.003702 | 0.048150 | 0.901693 | 3.417760 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 5.910790 | 0.317197 | -0.078388 | 0.310857 | 0.736502 | 0.287114 | 0.802403 | 5.910790 | 2.475566 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 3.081818 | -0.032529 | 0.277715 | 0.651463 | 0.344222 | 0.509781 | -0.054660 | 0.996880 | 3.081818 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 5.650194 | 0.117275 | 0.683878 | 0.707551 | 0.372227 | 0.148243 | -0.244354 | 2.739923 | 5.650194 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 4.254054 | 0.327538 | 0.416729 | 0.498445 | 0.709575 | 1.286902 | 1.054821 | 4.254054 | 1.129675 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 3.599117 | 0.023984 | 0.112546 | 0.326101 | 0.519696 | 0.358524 | 0.895500 | 3.599117 | 0.958826 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | nn Temporal Variability | 0.823007 | -0.189377 | 0.315704 | 0.423529 | 0.678306 | -0.003534 | 0.823007 | 0.761790 | 0.720404 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 3.090624 | 0.085710 | -0.036376 | 0.439197 | 0.208954 | 0.671797 | 0.124947 | 0.709424 | 3.090624 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 1.384075 | -0.080045 | 0.048573 | 0.542039 | 0.122211 | 0.667311 | 0.011845 | 1.126327 | 1.384075 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 4.870564 | -0.420854 | -0.232172 | -0.046775 | 0.474453 | -0.281477 | 0.738807 | 4.870564 | 1.052139 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 6.871010 | -0.535955 | -0.421470 | 0.495958 | 0.010561 | 0.739030 | -0.072713 | 1.362586 | 6.871010 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 3.430858 | -0.088798 | -0.357518 | 0.105867 | 0.503497 | 0.066897 | 1.406270 | 3.430858 | 0.565892 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | N03 | digital_ok | ee Temporal Discontinuties | 4.499473 | -0.280914 | -0.083115 | 0.535461 | 0.126652 | 0.491034 | 0.071087 | 1.110325 | 4.499473 |